arXiv subjects

Jason Wong

Publications and source records attributed to Jason Wong.

7 recordsLinked to original sources

arXiv2025

Dynamics of $E_6$ Chiral Gauge Theories

We present exact non-perturbative solutions to chiral gauge theories based on the $E_6$ gauge group and several matter fermions in the fundamental $\bf{27}$-dimensional representation. They are obtained when supersymmetric versions are perturbed by small supersymmetry breaking by anomaly mediation. The universality classes obtained are very different from what can be conjectured by the tumbling hypothesis. In particular, the case with three $\bf{27}$s may have an unbroken $\text{SU}(3)$ symmetry with massless composite fermions in $\bf{10}$ of $\text{SU}(3)$. For this case, we employed numerical techniques to obtain the exact ground state.

hep-th↗

arXiv2025

Exact Results in Chiral Gauge Theories with Flavor

We present exact results in softly-broken supersymmetric $\text{SU}(N_C)$ chiral gauge theories with charged fermions in one antisymmetric, $N_F$ fundamental, and $N_C+N_F-4$ anti-fundamental representations. We achieve this by considering the supersymmetric version of these theories and utilizing anomaly mediated supersymmetry breaking at a scale $m \ll \Lambda$ to generate a vacuum. The connection to non-supersymmetric theories is then conjectured in the limit $m \rightarrow \infty$. For odd $N_C$, we determine the massless fermions and unbroken global symmetries in the infrared. For even $N_C$, we find global symmetries are non-anomalous and no massless fermions. In all cases, the symmetry breaking patterns differ from what the tumbling hypothesis would suggest.

hep-th↗

arXiv2024

Evaluating Fairness in Transaction Fraud Models: Fairness Metrics, Bias Audits, and Challenges

Ensuring fairness in transaction fraud detection models is vital due to the potential harms and legal implications of biased decision-making. Despite extensive research on algorithmic fairness, there is a notable gap in the study of bias in fraud detection models, mainly due to the field's unique challenges. These challenges include the need for fairness metrics that account for fraud data's imbalanced nature and the tradeoff between fraud protection and service quality. To address this gap, we present a comprehensive fairness evaluation of transaction fraud models using public synthetic datasets, marking the first algorithmic bias audit in this domain. Our findings reveal three critical insights: (1) Certain fairness metrics expose significant bias only after normalization, highlighting the impact of class imbalance. (2) Bias is significant in both service quality-related parity metrics and fraud protection-related parity metrics. (3) The fairness through unawareness approach, which involved removing sensitive attributes such as gender, does not improve bias mitigation within these datasets, likely due to the presence of correlated proxies. We also discuss socio-technical fairness-related challenges in transaction fraud models. These insights underscore the need for a nuanced approach to fairness in fraud detection, balancing protection and service quality, and moving beyond simple bias mitigation strategies. Future work must focus on refining fairness metrics and developing methods tailored to the unique complexities of the transaction fraud domain.

cs.LG↗

arXiv2024

Locally Differentially Private Embedding Models in Distributed Fraud Prevention Systems

Global financial crime activity is driving demand for machine learning solutions in fraud prevention. However, prevention systems are commonly serviced to financial institutions in isolation, and few provisions exist for data sharing due to fears of unintentional leaks and adversarial attacks. Collaborative learning advances in finance are rare, and it is hard to find real-world insights derived from privacy-preserving data processing systems. In this paper, we present a collaborative deep learning framework for fraud prevention, designed from a privacy standpoint, and awarded at the recent PETs Prize Challenges. We leverage latent embedded representations of varied-length transaction sequences, along with local differential privacy, in order to construct a data release mechanism which can securely inform externally hosted fraud and anomaly detection models. We assess our contribution on two distributed data sets donated by large payment networks, and demonstrate robustness to popular inference-time attacks, along with utility-privacy trade-offs analogous to published work in alternative application domains.

cs.CR↗

arXiv2024

Towards a Foundation Purchasing Model: Pretrained Generative Autoregression on Transaction Sequences

Machine learning models underpin many modern financial systems for use cases such as fraud detection and churn prediction. Most are based on supervised learning with hand-engineered features, which relies heavily on the availability of labelled data. Large self-supervised generative models have shown tremendous success in natural language processing and computer vision, yet so far they haven't been adapted to multivariate time series of financial transactions. In this paper, we present a generative pretraining method that can be used to obtain contextualised embeddings of financial transactions. Benchmarks on public datasets demonstrate that it outperforms state-of-the-art self-supervised methods on a range of downstream tasks. We additionally perform large-scale pretraining of an embedding model using a corpus of data from 180 issuing banks containing 5.1 billion transactions and apply it to the card fraud detection problem on hold-out datasets. The embedding model significantly improves value detection rate at high precision thresholds and transfers well to out-of-domain distributions.

cs.LG↗

arXiv2022

Semi-Equivariant GNN Architectures for Jet Tagging

Composing Graph Neural Networks (GNNs) of operations that respect physical symmetries has been suggested to give better model performance with a smaller number of learnable parameters. However, real-world applications, such as in high energy physics have not born this out. We present the novel architecture VecNet that combines both symmetry-respecting and unconstrained operations to study and tune the degree of physics-informed GNNs. We introduce a novel metric, the \textit{ant factor}, to quantify the resource-efficiency of each configuration in the search-space. We find that a generalized architecture such as ours can deliver optimal performance in resource-constrained applications.

hep-ph↗

arXiv2021

Attribution of Predictive Uncertainties in Classification Models

Predictive uncertainties in classification tasks are often a consequence of model inadequacy or insufficient training data. In popular applications, such as image processing, we are often required to scrutinise these uncertainties by meaningfully attributing them to input features. This helps to improve interpretability assessments. However, there exist few effective frameworks for this purpose. Vanilla forms of popular methods for the provision of saliency masks, such as SHAP or integrated gradients, adapt poorly to target measures of uncertainty. Thus, state-of-the-art tools instead proceed by creating counterfactual or adversarial feature vectors, and assign attributions by direct comparison to original images. In this paper, we present a novel framework that combines path integrals, counterfactual explanations and generative models, in order to procure attributions that contain few observable artefacts or noise. We evidence that this outperforms existing alternatives through quantitative evaluations with popular benchmarking methods and data sets of varying complexity.

cs.LG↗